Key result
A predictive model combining heart rate variability and blood pressure features achieved 97.7% accuracy, 98.9% specificity, and 95.4% sensitivity for predicting mortality 0.5-1 hour before death.
Why the study?
Existing ICU mortality prediction methods often require recording extensive patient data that is impractical to obtain, prompting exploration of prediction using only heart rate variability and blood pressure.
Observational (n=88)
A predictive model combining heart rate variability and blood pressure features achieved high accuracy in predicting short-term mortality in cerebrovascular ICU patients.
May refine cerebrovascular ICU mortality prediction via timed signals; leaves open prospective validation before any clinical use.
This paper aimed to explore the mortality prediction of cerebrovascular patients in the intensive care unit (ICU) by examining the important signals associated with these patients during different periods of admission in the intensive care unit, which is considered one of the new topics in the medical field. Several approaches have been proposed for prediction in this area. Each of these methods has been able to predict mortality somewhat, but many of these techniques require recording a large amount of data from the patients, where recording all data is not possible in most cases; at the same time, this paper focuses only on heart rate variability (HRV) and systolic and diastolic blood pressure. The ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) Clinical Database. The proposed algorithm was evaluated using data from 88 Cerebrovascular ICU patients, 48 men and 40 women, during their first 48 hours of ICU stay. The electrocardiogram (ECG) signals are related to lead II, and the sampling frequency is 125 Hz. The time of admission and time of death are labeled in all data. In this study, the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood pressure. To predict the patient's future condition, the combination of features extracted from the return mapping generated by the HRV signal, such as angle (α), area (A), and various parameters generated by systolic and diastolic blood pressure, includingDBPMax−Min, SBPSD have been used. Also, to select the best feature combination, the genetic algorithm (GA) and mutual information (MI) methods were used. Paired sample t-test statistical analysis was used to compare the results of two episodes (death and non-death episode). The p-value for detecting the significance level was considered less than 0.005. The results indicate that the new approach presented in this paper can be compared with other methods or leads to better results. The best combination of features based on GA to achieve maximum predictive accuracy wasm,LMean, A,SBPSVMax,DBPMax−Min. The accuracy, specificity, and sensitivity based on the best features obtained of GA were 97.7%, 98.9%, and 95.4% for cerebral ischemia disease with a prediction horizon of 0.5-1 hour before death. The d-factor for the best feature combination based on the GA model is less than 1 (d-factor=0.95). Also, the Bracketed by 95PPU (%) was obtained at 98.6. This shows that the proposed model has optimum uncertainty in the mortality prediction of cerebrovascular patients. The combination of HRV and blood pressure signals could increase the accuracy of the diagnosis of the death episode and reduce the minimum time of hospitalization of the patient to determine the future status.
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Moridani et al. (2023) conducted an observational in Cerebrovascular disease / cerebral ischemia (n=88). Predictive model combining heart rate variability and blood pressure features was evaluated on Mortality prediction accuracy 0.5-1 hour before death. A predictive model combining heart rate variability and blood pressure features achieved 97.7% accuracy, 98.9% specificity, and 95.4% sensitivity for predicting mortality 0.5-1 hour before death.
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